Overview

Dataset statistics

Number of variables17
Number of observations9999
Missing cells3954
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory136.0 B

Variable types

Numeric12
Categorical5

Alerts

name has a high cardinality: 9791 distinct values High cardinality
host_name has a high cardinality: 2851 distinct values High cardinality
last_review has a high cardinality: 2010 distinct values High cardinality
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
price is highly correlated with log_priceHigh correlation
minimum_nights is highly correlated with number_of_reviewsHigh correlation
number_of_reviews is highly correlated with minimum_nights and 3 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
availability_365 is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
log_price is highly correlated with priceHigh correlation
price is highly correlated with log_priceHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
log_price is highly correlated with priceHigh correlation
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
price is highly correlated with log_priceHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
log_price is highly correlated with priceHigh correlation
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
neighbourhood is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with neighbourhood and 1 other fieldsHigh correlation
longitude is highly correlated with neighbourhood and 1 other fieldsHigh correlation
price is highly correlated with log_priceHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
log_price is highly correlated with priceHigh correlation
last_review has 1958 (19.6%) missing values Missing
reviews_per_month has 1958 (19.6%) missing values Missing
price is highly skewed (γ1 = 26.55905346) Skewed
name is uniformly distributed Uniform
id has unique values Unique
number_of_reviews has 1958 (19.6%) zeros Zeros
availability_365 has 5721 (57.2%) zeros Zeros
number_of_reviews_ltm has 6974 (69.7%) zeros Zeros

Reproduction

Analysis started2022-02-17 15:05:03.922068
Analysis finished2022-02-17 15:05:30.470097
Duration26.55 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct9999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4887275.145
Minimum5396
Maximum9492265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:30.554098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5396
5-th percentile554565.2
Q12567391
median5149727
Q37155202
95-th percentile8987572.9
Maximum9492265
Range9486869
Interquartile range (IQR)4587811

Descriptive statistics

Standard deviation2701832.897
Coefficient of variation (CV)0.5528301183
Kurtosis-1.198620243
Mean4887275.145
Median Absolute Deviation (MAD)2181178
Skewness-0.163297159
Sum4.886786418 × 1010
Variance7.299901001 × 1012
MonotonicityStrictly increasing
2022-02-17T16:05:30.697087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69863671
 
< 0.1%
76417821
 
< 0.1%
67099291
 
< 0.1%
70580911
 
< 0.1%
78477201
 
< 0.1%
46762701
 
< 0.1%
73763621
 
< 0.1%
92478951
 
< 0.1%
94330681
 
< 0.1%
72956711
 
< 0.1%
Other values (9989)9989
99.9%
ValueCountFrequency (%)
53961
< 0.1%
73971
< 0.1%
79641
< 0.1%
93591
< 0.1%
99521
< 0.1%
105861
< 0.1%
105881
< 0.1%
109171
< 0.1%
112131
< 0.1%
112651
< 0.1%
ValueCountFrequency (%)
94922651
< 0.1%
94898951
< 0.1%
94881901
< 0.1%
94877441
< 0.1%
94877391
< 0.1%
94876191
< 0.1%
94873601
< 0.1%
94872321
< 0.1%
94864451
< 0.1%
94863881
< 0.1%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct9791
Distinct (%)98.2%
Missing30
Missing (%)0.3%
Memory size78.2 KiB
StudiointheheartofParis
 
8
CosyflatinMontmartre
 
7
CharmingflatnearMontmartre
 
6
CharmingflatintheheartofParis
 
5
CharmingflatinMontmartre
 
5
Other values (9786)
9938 

Length

Max length68
Median length28
Mean length28.24606279
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9665 ?
Unique (%)97.0%

Sample

1st rowExploretheheartofoldParis
2nd rowMARAIS-2ROOMSAPT-2/4PEOPLE
3rd rowLarge&sunnyflatwithbalcony!
4th rowCozy,CentralParis:WALKorVELIBEVERYWHERE!
5th rowParispetitcoindouillet

Common Values

ValueCountFrequency (%)
StudiointheheartofParis8
 
0.1%
CosyflatinMontmartre7
 
0.1%
CharmingflatnearMontmartre6
 
0.1%
CharmingflatintheheartofParis5
 
0.1%
CharmingflatinMontmartre5
 
0.1%
AppartementprocheTourEiffel4
 
< 0.1%
NicestudionearMontmartre4
 
< 0.1%
CharmingapartmentinParis4
 
< 0.1%
Appartementdecharme4
 
< 0.1%
ApartmentintheheartofParis3
 
< 0.1%
Other values (9781)9919
99.2%
(Missing)30
 
0.3%

Length

2022-02-17T16:05:30.895085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
studiointheheartofparis10
 
0.1%
charmingflatnearmontmartre8
 
0.1%
charmingflatinmontmartre7
 
0.1%
cosyflatinmontmartre7
 
0.1%
cosyflatintheheartofparis7
 
0.1%
charmingflatintheheartofparis5
 
0.1%
appartementprochetoureiffel5
 
0.1%
charmingparisianapartment5
 
0.1%
cosystudiointheheartofparis5
 
0.1%
charmantstudioparisien5
 
0.1%
Other values (9670)9907
99.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

host_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9339
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18286906.49
Minimum2626
Maximum421961718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:31.068085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2626
5-th percentile1328190
Q16049837.5
median14081535
Q328230128.5
95-th percentile42786637
Maximum421961718
Range421959092
Interquartile range (IQR)22180291

Descriptive statistics

Standard deviation21472593.69
Coefficient of variation (CV)1.17420591
Kurtosis158.8444001
Mean18286906.49
Median Absolute Deviation (MAD)9709251
Skewness9.782402755
Sum1.82850778 × 1011
Variance4.610722797 × 1014
MonotonicityNot monotonic
2022-02-17T16:05:31.225101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
764279263
 
0.6%
15224227
 
0.3%
65638620
 
0.2%
502716420
 
0.2%
760837918
 
0.2%
397174315
 
0.2%
76122709
 
0.1%
244952838
 
0.1%
130136338
 
0.1%
103417878
 
0.1%
Other values (9329)9803
98.0%
ValueCountFrequency (%)
26262
< 0.1%
79031
 
< 0.1%
206331
 
< 0.1%
221551
 
< 0.1%
284221
 
< 0.1%
296661
 
< 0.1%
335341
 
< 0.1%
371073
< 0.1%
388182
< 0.1%
394021
 
< 0.1%
ValueCountFrequency (%)
4219617181
 
< 0.1%
4219610572
< 0.1%
4181321543
< 0.1%
4088600991
 
< 0.1%
4021913114
< 0.1%
3492138631
 
< 0.1%
3313272281
 
< 0.1%
3263587251
 
< 0.1%
3113729201
 
< 0.1%
2910073693
< 0.1%

host_name
Categorical

HIGH CARDINALITY

Distinct2851
Distinct (%)28.5%
Missing8
Missing (%)0.1%
Memory size78.2 KiB
Marie
 
109
Anne
 
87
Pierre
 
81
Sophie
 
81
Nicolas
 
77
Other values (2846)
9556 

Length

Max length28
Median length6
Mean length6.947252527
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1825 ?
Unique (%)18.3%

Sample

1st rowBorzou
2nd rowFranck
3rd rowAnaïs
4th rowBernadette
5th rowElisabeth

Common Values

ValueCountFrequency (%)
Marie109
 
1.1%
Anne87
 
0.9%
Pierre81
 
0.8%
Sophie81
 
0.8%
Nicolas77
 
0.8%
Julien74
 
0.7%
Ludovic72
 
0.7%
Claire71
 
0.7%
Thomas71
 
0.7%
Catherine70
 
0.7%
Other values (2841)9198
92.0%

Length

2022-02-17T16:05:31.374088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
marie109
 
1.1%
anne87
 
0.9%
pierre81
 
0.8%
sophie81
 
0.8%
nicolas77
 
0.8%
julien74
 
0.7%
ludovic72
 
0.7%
claire71
 
0.7%
thomas71
 
0.7%
catherine70
 
0.7%
Other values (2839)9198
92.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

neighbourhood
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Buttes-Montmartre
1133 
Popincourt
1001 
Entrepôt
768 
Vaugirard
730 
Ménilmontant
645 
Other values (15)
5722 

Length

Max length19
Median length10
Mean length10.67706771
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHôtel-de-Ville
2nd rowHôtel-de-Ville
3rd rowOpéra
4th rowLouvre
5th rowPopincourt

Common Values

ValueCountFrequency (%)
Buttes-Montmartre1133
 
11.3%
Popincourt1001
 
10.0%
Entrepôt768
 
7.7%
Vaugirard730
 
7.3%
Ménilmontant645
 
6.5%
Batignolles-Monceau593
 
5.9%
Buttes-Chaumont590
 
5.9%
Opéra473
 
4.7%
Temple449
 
4.5%
Passy448
 
4.5%
Other values (10)3169
31.7%

Length

2022-02-17T16:05:31.505088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buttes-montmartre1133
 
11.3%
popincourt1001
 
10.0%
entrepôt768
 
7.7%
vaugirard730
 
7.3%
ménilmontant645
 
6.5%
batignolles-monceau593
 
5.9%
buttes-chaumont590
 
5.9%
opéra473
 
4.7%
temple449
 
4.5%
passy448
 
4.5%
Other values (10)3169
31.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5282
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.86395155
Minimum48.81714
Maximum48.90075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:31.643085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum48.81714
5-th percentile48.832198
Q148.851025
median48.86492
Q348.87883
95-th percentile48.891311
Maximum48.90075
Range0.08361
Interquartile range (IQR)0.027805

Descriptive statistics

Standard deviation0.01813592432
Coefficient of variation (CV)0.000371151406
Kurtosis-0.7580695136
Mean48.86395155
Median Absolute Deviation (MAD)0.0139
Skewness-0.2302105864
Sum488590.6515
Variance0.000328911751
MonotonicityNot monotonic
2022-02-17T16:05:31.809088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.880928
 
0.1%
48.863718
 
0.1%
48.868458
 
0.1%
48.84777
 
0.1%
48.864797
 
0.1%
48.860417
 
0.1%
48.868687
 
0.1%
48.870857
 
0.1%
48.858177
 
0.1%
48.861147
 
0.1%
Other values (5272)9926
99.3%
ValueCountFrequency (%)
48.817141
< 0.1%
48.817231
< 0.1%
48.817241
< 0.1%
48.817921
< 0.1%
48.818371
< 0.1%
48.818821
< 0.1%
48.818921
< 0.1%
48.818951
< 0.1%
48.819021
< 0.1%
48.819171
< 0.1%
ValueCountFrequency (%)
48.900751
< 0.1%
48.900681
< 0.1%
48.899811
< 0.1%
48.899741
< 0.1%
48.899341
< 0.1%
48.899271
< 0.1%
48.899251
< 0.1%
48.899121
< 0.1%
48.8991
< 0.1%
48.898951
< 0.1%

longitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6786
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.347086234
Minimum2.23549
Maximum2.46705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:31.983087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.23549
5-th percentile2.286808
Q12.32767
median2.34976
Q32.371705
95-th percentile2.396456
Maximum2.46705
Range0.23156
Interquartile range (IQR)0.044035

Descriptive statistics

Standard deviation0.03266944532
Coefficient of variation (CV)0.01391914999
Kurtosis-0.262756909
Mean2.347086234
Median Absolute Deviation (MAD)0.022
Skewness-0.4132909795
Sum23468.51525
Variance0.001067292657
MonotonicityNot monotonic
2022-02-17T16:05:32.124087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.337198
 
0.1%
2.348177
 
0.1%
2.347286
 
0.1%
2.353076
 
0.1%
2.358356
 
0.1%
2.381946
 
0.1%
2.351145
 
0.1%
2.349855
 
0.1%
2.349775
 
0.1%
2.349795
 
0.1%
Other values (6776)9940
99.4%
ValueCountFrequency (%)
2.235491
< 0.1%
2.237391
< 0.1%
2.238381
< 0.1%
2.248431
< 0.1%
2.25111
< 0.1%
2.251621
< 0.1%
2.251731
< 0.1%
2.2518151
< 0.1%
2.252011
< 0.1%
2.252581
< 0.1%
ValueCountFrequency (%)
2.467051
< 0.1%
2.465741
< 0.1%
2.461921
< 0.1%
2.440711
< 0.1%
2.432621
< 0.1%
2.428721
< 0.1%
2.422581
< 0.1%
2.422421
< 0.1%
2.421771
< 0.1%
2.415331
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Entirehome/apt
8620 
Privateroom
1335 
Sharedroom
 
40
Hotelroom
 
4

Length

Max length14
Median length14
Mean length13.58145815
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntirehome/apt
2nd rowEntirehome/apt
3rd rowEntirehome/apt
4th rowEntirehome/apt
5th rowEntirehome/apt

Common Values

ValueCountFrequency (%)
Entirehome/apt8620
86.2%
Privateroom1335
 
13.4%
Sharedroom40
 
0.4%
Hotelroom4
 
< 0.1%

Length

2022-02-17T16:05:32.278100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-17T16:05:32.364100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
entirehome/apt8620
86.2%
privateroom1335
 
13.4%
sharedroom40
 
0.4%
hotelroom4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct418
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.3091309
Minimum8
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:32.489089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile37.9
Q160
median83
Q3125
95-th percentile265
Maximum8000
Range7992
Interquartile range (IQR)65

Descriptive statistics

Standard deviation188.0536918
Coefficient of variation (CV)1.645132723
Kurtosis983.9845252
Mean114.3091309
Median Absolute Deviation (MAD)28
Skewness26.55905346
Sum1142977
Variance35364.19099
MonotonicityNot monotonic
2022-02-17T16:05:32.940089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60481
 
4.8%
70450
 
4.5%
80441
 
4.4%
50423
 
4.2%
100393
 
3.9%
90370
 
3.7%
65296
 
3.0%
75288
 
2.9%
120275
 
2.8%
150236
 
2.4%
Other values (408)6346
63.5%
ValueCountFrequency (%)
82
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
131
 
< 0.1%
152
 
< 0.1%
172
 
< 0.1%
192
 
< 0.1%
2017
0.2%
213
 
< 0.1%
ValueCountFrequency (%)
80002
< 0.1%
70001
< 0.1%
67421
< 0.1%
40001
< 0.1%
30001
< 0.1%
24501
< 0.1%
23251
< 0.1%
22001
< 0.1%
20001
< 0.1%
16151
< 0.1%

minimum_nights
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.8558856
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:33.087085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median8
Q3365
95-th percentile365
Maximum9999
Range9998
Interquartile range (IQR)362

Descriptive statistics

Standard deviation201.925913
Coefficient of variation (CV)1.295593761
Kurtosis562.7964213
Mean155.8558856
Median Absolute Deviation (MAD)7
Skewness11.82121482
Sum1558403
Variance40774.07435
MonotonicityNot monotonic
2022-02-17T16:05:33.223086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3654092
40.9%
31238
 
12.4%
21170
 
11.7%
1793
 
7.9%
4674
 
6.7%
5583
 
5.8%
30449
 
4.5%
7308
 
3.1%
6230
 
2.3%
9158
 
0.6%
Other values (47)404
 
4.0%
ValueCountFrequency (%)
1793
7.9%
21170
11.7%
31238
12.4%
4674
6.7%
5583
5.8%
6230
 
2.3%
7308
 
3.1%
822
 
0.2%
98
 
0.1%
1052
 
0.5%
ValueCountFrequency (%)
99991
 
< 0.1%
10002
 
< 0.1%
5001
 
< 0.1%
4002
 
< 0.1%
3654092
40.9%
3602
 
< 0.1%
3001
 
< 0.1%
2706
 
0.1%
1831
 
< 0.1%
1807
 
0.1%

number_of_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct396
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.86358636
Minimum0
Maximum823
Zeros1958
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:33.356213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12
Q347
95-th percentile174.1
Maximum823
Range823
Interquartile range (IQR)46

Descriptive statistics

Standard deviation69.88208565
Coefficient of variation (CV)1.753030573
Kurtosis18.44843883
Mean39.86358636
Median Absolute Deviation (MAD)12
Skewness3.577723914
Sum398596
Variance4883.505894
MonotonicityNot monotonic
2022-02-17T16:05:33.476085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01958
 
19.6%
1663
 
6.6%
2458
 
4.6%
3333
 
3.3%
4275
 
2.8%
5245
 
2.5%
6199
 
2.0%
8172
 
1.7%
9170
 
1.7%
7168
 
1.7%
Other values (386)5358
53.6%
ValueCountFrequency (%)
01958
19.6%
1663
 
6.6%
2458
 
4.6%
3333
 
3.3%
4275
 
2.8%
5245
 
2.5%
6199
 
2.0%
7168
 
1.7%
8172
 
1.7%
9170
 
1.7%
ValueCountFrequency (%)
8231
< 0.1%
8171
< 0.1%
7721
< 0.1%
6951
< 0.1%
6761
< 0.1%
6741
< 0.1%
6471
< 0.1%
6211
< 0.1%
5932
< 0.1%
5892
< 0.1%

last_review
Categorical

HIGH CARDINALITY
MISSING

Distinct2010
Distinct (%)25.0%
Missing1958
Missing (%)19.6%
Memory size78.2 KiB
02-01-2022
 
195
01-01-2022
 
108
03-01-2022
 
93
30-12-2021
 
80
12-12-2021
 
75
Other values (2005)
7490 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique713 ?
Unique (%)8.9%

Sample

1st row04-01-2022
2nd row30-12-2021
3rd row14-09-2015
4th row28-12-2021
5th row27-09-2021

Common Values

ValueCountFrequency (%)
02-01-2022195
 
2.0%
01-01-2022108
 
1.1%
03-01-202293
 
0.9%
30-12-202180
 
0.8%
12-12-202175
 
0.8%
23-12-202173
 
0.7%
26-12-202169
 
0.7%
31-12-202166
 
0.7%
19-12-202159
 
0.6%
22-12-202155
 
0.6%
Other values (2000)7168
71.7%
(Missing)1958
 
19.6%

Length

2022-02-17T16:05:33.602087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02-01-2022195
 
2.4%
01-01-2022108
 
1.3%
03-01-202293
 
1.2%
30-12-202180
 
1.0%
12-12-202175
 
0.9%
23-12-202173
 
0.9%
26-12-202169
 
0.9%
31-12-202166
 
0.8%
19-12-202159
 
0.7%
22-12-202155
 
0.7%
Other values (2000)7168
89.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reviews_per_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct428
Distinct (%)5.3%
Missing1958
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean0.5801392862
Minimum0.01
Maximum8.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:33.728088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.08
median0.28
Q30.74
95-th percentile2.21
Maximum8.14
Range8.13
Interquartile range (IQR)0.66

Descriptive statistics

Standard deviation0.8272541979
Coefficient of variation (CV)1.4259579
Kurtosis13.98639877
Mean0.5801392862
Median Absolute Deviation (MAD)0.24
Skewness3.113904174
Sum4664.9
Variance0.684349508
MonotonicityNot monotonic
2022-02-17T16:05:33.885088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01516
 
5.2%
0.03361
 
3.6%
0.04287
 
2.9%
0.05242
 
2.4%
0.02206
 
2.1%
0.06180
 
1.8%
0.09161
 
1.6%
0.07153
 
1.5%
0.08153
 
1.5%
0.1142
 
1.4%
Other values (418)5640
56.4%
(Missing)1958
 
19.6%
ValueCountFrequency (%)
0.01516
5.2%
0.02206
 
2.1%
0.03361
3.6%
0.04287
2.9%
0.05242
2.4%
0.06180
 
1.8%
0.07153
 
1.5%
0.08153
 
1.5%
0.09161
 
1.6%
0.1142
 
1.4%
ValueCountFrequency (%)
8.141
< 0.1%
8.091
< 0.1%
7.921
< 0.1%
7.711
< 0.1%
7.581
< 0.1%
7.431
< 0.1%
7.361
< 0.1%
7.281
< 0.1%
7.011
< 0.1%
6.931
< 0.1%

calculated_host_listings_count
Real number (ℝ≥0)

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.457045705
Minimum1
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:34.046089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum265
Range264
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.73795527
Coefficient of variation (CV)5.130957698
Kurtosis107.3835758
Mean3.457045705
Median Absolute Deviation (MAD)0
Skewness9.996236457
Sum34567
Variance314.6350571
MonotonicityNot monotonic
2022-02-17T16:05:34.177090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
18550
85.5%
2814
 
8.1%
3167
 
1.7%
19763
 
0.6%
547
 
0.5%
444
 
0.4%
6927
 
0.3%
3626
 
0.3%
2322
 
0.2%
8120
 
0.2%
Other values (31)219
 
2.2%
ValueCountFrequency (%)
18550
85.5%
2814
 
8.1%
3167
 
1.7%
444
 
0.4%
547
 
0.5%
619
 
0.2%
712
 
0.1%
816
 
0.2%
910
 
0.1%
1011
 
0.1%
ValueCountFrequency (%)
2654
 
< 0.1%
19763
0.6%
875
 
0.1%
854
 
< 0.1%
826
 
0.1%
8120
 
0.2%
713
 
< 0.1%
6927
0.3%
612
 
< 0.1%
528
 
0.1%

availability_365
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct366
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.65736574
Minimum0
Maximum365
Zeros5721
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:34.321089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3179
95-th percentile363
Maximum365
Range365
Interquartile range (IQR)179

Descriptive statistics

Standard deviation129.4912291
Coefficient of variation (CV)1.47724299
Kurtosis-0.3544987148
Mean87.65736574
Median Absolute Deviation (MAD)0
Skewness1.12418884
Sum876486
Variance16767.97841
MonotonicityNot monotonic
2022-02-17T16:05:34.445085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05721
57.2%
365280
 
2.8%
364143
 
1.4%
36395
 
1.0%
35867
 
0.7%
866
 
0.7%
963
 
0.6%
36256
 
0.6%
19341
 
0.4%
19240
 
0.4%
Other values (356)3427
34.3%
ValueCountFrequency (%)
05721
57.2%
136
 
0.4%
233
 
0.3%
310
 
0.1%
416
 
0.2%
519
 
0.2%
615
 
0.2%
725
 
0.3%
866
 
0.7%
963
 
0.6%
ValueCountFrequency (%)
365280
2.8%
364143
1.4%
36395
 
1.0%
36256
 
0.6%
36110
 
0.1%
36012
 
0.1%
35919
 
0.2%
35867
 
0.7%
35717
 
0.2%
3569
 
0.1%

number_of_reviews_ltm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.522552255
Minimum0
Maximum97
Zeros6974
Zeros (%)69.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:34.600087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile14
Maximum97
Range97
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.743171399
Coefficient of variation (CV)2.673154296
Kurtosis34.0378495
Mean2.522552255
Median Absolute Deviation (MAD)0
Skewness4.921242881
Sum25223
Variance45.47036052
MonotonicityNot monotonic
2022-02-17T16:05:34.752088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06974
69.7%
1495
 
5.0%
2401
 
4.0%
3299
 
3.0%
4250
 
2.5%
5208
 
2.1%
6162
 
1.6%
8144
 
1.4%
7131
 
1.3%
995
 
1.0%
Other values (62)840
 
8.4%
ValueCountFrequency (%)
06974
69.7%
1495
 
5.0%
2401
 
4.0%
3299
 
3.0%
4250
 
2.5%
5208
 
2.1%
6162
 
1.6%
7131
 
1.3%
8144
 
1.4%
995
 
1.0%
ValueCountFrequency (%)
971
< 0.1%
951
< 0.1%
801
< 0.1%
771
< 0.1%
751
< 0.1%
701
< 0.1%
671
< 0.1%
661
< 0.1%
651
< 0.1%
641
< 0.1%

log_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct418
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.490776574
Minimum2.079441542
Maximum8.987196821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-02-17T16:05:34.902086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.079441542
5-th percentile3.634919335
Q14.094344562
median4.418840608
Q34.828313737
95-th percentile5.579729826
Maximum8.987196821
Range6.907755279
Interquartile range (IQR)0.7339691751

Descriptive statistics

Standard deviation0.6148088419
Coefficient of variation (CV)0.136904794
Kurtosis2.365150144
Mean4.490776574
Median Absolute Deviation (MAD)0.368651135
Skewness0.8514394563
Sum44903.27497
Variance0.377989912
MonotonicityNot monotonic
2022-02-17T16:05:35.048091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.094344562481
 
4.8%
4.248495242450
 
4.5%
4.382026635441
 
4.4%
3.912023005423
 
4.2%
4.605170186393
 
3.9%
4.49980967370
 
3.7%
4.17438727296
 
3.0%
4.317488114288
 
2.9%
4.787491743275
 
2.8%
4.007333185236
 
2.4%
Other values (408)6346
63.5%
ValueCountFrequency (%)
2.0794415422
 
< 0.1%
2.1972245771
 
< 0.1%
2.3025850931
 
< 0.1%
2.3978952731
 
< 0.1%
2.5649493571
 
< 0.1%
2.7080502012
 
< 0.1%
2.8332133442
 
< 0.1%
2.9444389792
 
< 0.1%
2.99573227417
0.2%
3.0445224383
 
< 0.1%
ValueCountFrequency (%)
8.9871968212
< 0.1%
8.8536654281
< 0.1%
8.8161118961
< 0.1%
8.294049641
< 0.1%
8.0063675681
< 0.1%
7.8038433041
< 0.1%
7.7514753181
< 0.1%
7.6962126391
< 0.1%
7.600902461
< 0.1%
7.3870902361
< 0.1%

Interactions

2022-02-17T16:05:27.516089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:06.359066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:08.200067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:10.018091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:12.074086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:13.872091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:15.875090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:17.693087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:19.739088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:21.646085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:23.450087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:25.352091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:27.649090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:06.478068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:08.337076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:10.141088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:12.210088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:14.039087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:16.023088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:17.835086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:19.894088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:21.774087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:23.600087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:25.488098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:27.798087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:06.600068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:08.479068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:10.280091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:12.347086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:14.206087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:16.166088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:17.978086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:20.045090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:21.928091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:23.759088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:25.665090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:27.954088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:06.766070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:08.617069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:10.724084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:12.487092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:14.353089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:16.338088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:18.136086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:20.187087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:22.085088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:23.930091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:25.818086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:28.102088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:06.928068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:08.748068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:10.875089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:12.612085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:14.516098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:16.478089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-17T16:05:24.102088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:25.968088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-17T16:05:07.094066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:08.941067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-17T16:05:14.696100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:16.620086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:18.431088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:20.505090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:22.399086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:24.265090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:26.129085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:28.444087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:07.249068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:09.095071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:11.180090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:12.916088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:14.877089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:16.754088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:18.889091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:20.677087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:22.546084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:24.420086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:26.268089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:28.590085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:07.370069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:09.249068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:11.299090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:13.076089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:15.034090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:16.914086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:19.031087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:20.832090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:22.687086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:24.570086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:26.401087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:28.759089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:07.518079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:09.422067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:11.459088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:13.249087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:15.199087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:17.082088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-17T16:05:29.091087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:07.840066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-17T16:05:23.159090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:25.057091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-02-17T16:05:29.224087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:08.017066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:09.868070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:11.911087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:13.688089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:15.680089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:17.536087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:19.591089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:21.484087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:23.299090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:25.202097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-17T16:05:27.348085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-02-17T16:05:35.180087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-17T16:05:35.428091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-17T16:05:35.677088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-17T16:05:35.930086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-17T16:05:36.074086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-17T16:05:29.479086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-17T16:05:29.899087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-17T16:05:30.148085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-17T16:05:30.316086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlog_price
05396ExploretheheartofoldParis7903BorzouHôtel-de-Ville48.8524702.358350Entirehome/apt80227704-01-20221.82166454.382027
17397MARAIS-2ROOMSAPT-2/4PEOPLE2626FranckHôtel-de-Ville48.8590902.353150Entirehome/apt1051029030-12-20212.214212204.653960
27964Large&sunnyflatwithbalcony!22155AnaïsOpéra48.8741702.342450Entirehome/apt1306614-09-20150.04136504.867534
39359Cozy,CentralParis:WALKorVELIBEVERYWHERE!28422BernadetteLouvre48.8600602.348630Entirehome/apt751800NaNNaN114804.317488
49952Parispetitcoindouillet33534ElisabethPopincourt48.8637302.370930Entirehome/apt8043428-12-20210.32121284.382027
510586Studio7Montmartre37107MichaelButtes-Montmartre48.8890202.346560Entirehome/apt80304927-09-20210.33417314.382027
610588Studio10Montmartre37107MichaelButtes-Montmartre48.8891802.344900Entirehome/apt75301910-09-20210.14420434.317488
710917ELYSEES-PONCELETFLATNEARCH.ELYS39402IsabelleBatignolles-Monceau48.8790742.296904Entirehome/apt143302501-12-20160.171004.962845
811213DOWNTOWNPARIS41322MathieuEntrepôt48.8710902.373760Privateroom157115227-12-20211.91228435.056246
911265ElegantappartmentinMontmartre41718SylvieButtes-Montmartre48.8849402.339970Entirehome/apt10071601-10-20190.24112204.605170

Last rows

idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlog_price
99899486388Charmingflatwithbeautifulview13729717LouiseButtes-Montmartre48.885842.36541Entirehome/apt3251216-08-20180.171003.465736
99909486445AmazingviewstudioNotreDame49166922HélèneHôtel-de-Ville48.854022.35093Entirehome/apt120213319-12-20211.891282254.787492
99919487232NaN27822742AymericVaugirard48.839312.30737Entirehome/apt65365217-05-20160.031004.174387
99929487360Well-appointedSt.Michelstudio3399907MelissaLuxembourg48.852182.34163Entirehome/apt7536518630-09-20212.65235654.317488
99939487619Flatforfamily11678014SoniaBatignolles-Monceau48.888352.29381Entirehome/apt993650NaNNaN1004.595120
99949487739Opéracharmantappartement!10714020MartinOpéra48.874262.32775Entirehome/apt503651516-10-20160.201003.912023
99959487744Studiolumineuxetbienagencé49174455CharlotteBatignolles-Monceau48.889732.32476Entirehome/apt503650NaNNaN1003.912023
99969488190CosyBright&SereneStudio.49176747JesusButtes-Chaumont48.876492.38633Entirehome/apt70302716-12-20200.38118304.248495
99979489895LovelyStudionearBastille49184991SimonReuilly48.851102.37360Entirehome/apt52365128-11-20150.011003.951244
99989492265CosyflatinMontmartre15715264SophieButtes-Montmartre48.894392.34173Entirehome/apt753650NaNNaN1004.317488